Search Results (403)

Hybrid electric mining trucks operating in open-pit environments encounter highly variable gradients and payload conditions that standard energy management strategies fail to address adequately. Existing approaches are predominantly calibrated for full-load scenarios and neglect the accelerated battery degradation resulting from sustained high-power cycling, undermining long-term operational viability. This study presents a multi-objective energy management framework that couples real-time driving condition recognition with dynamic programming (DP) optimization for a 130-tonne hybrid mining truck. Field data collected from an open-pit mine in Heilongjiang Province were used to construct six physically representative driving conditions via principal component analysis and K-means clustering. A Bidirectional Gated Recurrent Unit (Bi-GRU) network (2 layers, 128 hidden units per direction) was trained on a route-based temporal split, attaining 95.8% classification accuracy across all six conditions. Condition-specific powertrain modes were subsequently defined, and a DP formulation with a weighted-sum cost function was solved to jointly minimize diesel consumption and battery capacity fade—quantified through a semi-empirical effective electric quantity metric. A marginal rate of substitution (MRS) analysis was conducted to identify the optimal trade-off between fuel economy and battery life preservation. In the DP cost function, the weight coefficient μ (ranging from 0 to 1) governs the relative emphasis placed on battery degradation minimization versus fuel consumption minimization: μ = 0 corresponds to pure fuel minimization, whereas μ = 1 corresponds to pure battery degradation minimization. The MRS analysis identified μ = 0.1 as the knee point of the Pareto trade-off: relative to pure fuel minimization (μ = 0), this setting reduces effective electric quantity by 6.1% while increasing fuel consumption by only 1.4% (MRS = 4.36). Against a rule-based baseline, the proposed strategy improves fuel economy by 12.3% and extends battery service life by 15.7%. Co-simulation results were validated against onboard fuel-flow measurements; absolute simulated and measured fuel consumption values are reported route-by-route, with deviations within 4.5%. A three-layer BP neural network (3 inputs, two hidden layers of 20 and 10 neurons, 1 output) trained on the DP solution reproduces near-optimal performance—with fuel consumption and effective electric quantity increases below 1.0% and 1.1%, respectively—while reducing computation time by over 96% (from approximately 52,860 s to 1836 s for the 1800 s driving cycle), demonstrating practical feasibility for real-time deployment. Full article

In this paper, a novel multiband microwave resonator is proposed and investigated for non-invasive glucose sensing applications. The structure is based on a compact, planar spiral-like geometry fed by a Coplanar waveguide (CPW) transmission line, designed to support multiple resonant modes through nested concentric rings. A full electromagnetic model was developed to predict the resonance behavior analytically, achieving excellent agreement with Computer Simulated Technology (CST) simulations across four resonant frequencies (2.7, 6.44, 8.0, and 12.8 GHz). The sensor demonstrated high glucose sensitivity at multiple frequencies, with peak values reaching 0.05 dB/mg/dL and 0.038 dB/mg/dL at 10.1 GHz and 6.22 GHz, respectively. To enhance conformability and skin contact, the antenna was further transformed into a semi-cylindrical flexible form suitable for finger-wrapping. Despite the mechanical deformation, the structure preserved its resonance while offering enhanced near-field interaction with biological tissues. The folded sensor achieved a sensitivity of 0.032 dB/mg/dL at 5.25 GHz and a peak gain of 6.05 dB, validating its robustness for wearable deployment. The clear correlation between reflection magnitude and glucose level (with R > 0.99) confirms the sensor’s potential as a passive, multiband, and non-invasive glucose monitoring platform. The physics-informed residual deep learning framework significantly enhances prediction accuracy, achieving an RMSE of 0.28 mg/dL, MARD of 0.13%, and confining 100% of both training and holdout predictions within the <5% ISO-like risk region, thereby ensuring robust and clinically reliable non-invasive glucose estimation. Full article

Hypoxia has become increasingly frequent in the semi-enclosed Bohai Sea since the early 2000s, posing significant risks to marine ecosystems. To address the limitations of existing dissolved oxygen models—particularly their poor predictive ability and lack of interpretability—we developed a two-month lead probabilistic forecasting framework for summer hypoxia using only multi-source remote sensing and reanalysis data, supplemented by in situ observations for validation. Environmental conditions in June were used to predict hypoxia probability in August via machine learning; among the seven algorithms tested, the optimized Random Forest model achieved the best performance (F1 = 0.76 and AUC = 0.92 on the independent test set). The model successfully reproduced observed hypoxia patterns in 2019 (validated against numerical simulations) and 2022 (validated against field measurements), capturing an increase in hypoxic area from 8229 km² to 13,866 km², which is consistent with intensifying thermal stratification under climate warming. SHAP-based interpretability analysis identified reduced wind speed and enhanced thermal stratification as the dominant physical drivers, highlighting the critical role of suppressed vertical mixing in limiting bottom-water oxygen supply. This study demonstrates that a physics-informed, interpretable machine learning approach based solely on satellite and reanalysis data can deliver reliable, early, and physically consistent hypoxia forecasts, offering a scalable solution for environmental monitoring of data-limited coastal seas. Full article

This paper uses actual evaporation and phreatic evaporation as the upper and lower boundary fluxes, respectively. It considers the exponential change in hydraulic conductivity with depth and uses the one-dimensional Richards equation to perform vertical discretization calculations on the soil to determine soil water deficit. A semi-analytical solution method is employed to accelerate the calculation speed. Based on the relationship between groundwater depth and topographic index, the spatial distribution of soil water deficit is obtained from the spatial distribution of the topographic index. This leads to the development of a new distributed Xin’anjiang model for hilly areas that considers non-steady-state evaporation. The model is applied to simulate soil moisture content in the typical Tarrawarra catchment and compared with the storage capacity model and the DHSVM model. It is found that the new distributed Xin’anjiang model developed in this paper shows significantly better performance in simulating soil moisture content than the storage capacity model and the DHSVM model. The new distributed Xin’anjiang model developed in this paper takes into account the physical mechanisms, calculation speed, and computational accuracy. It also considers the hydrodynamic characteristics of the unsaturated zone and the impact of non-steady-state evaporation. Full article

With the expansion of electric vehicle (EV) applications into unstructured sandy terrains such as deserts, accurately characterizing tire–sand dynamic interactions is essential for enhancing off-road performance. However, traditional terramechanics models, the discrete element method (DEM), and purely data-driven neural networks all have inherent limitations, failing to balance physical interpretability and computational efficiency. This study proposes a wheel–sand dynamics prediction model that integrates DEM simulation, semi-physical modeling, and deep learning. A DEM tire–sand contact platform is built to acquire longitudinal slip and cornering properties, and a dimensionless semi-physical tire model is derived using frictional constitutive relations and tire theory. A 3-DOF vehicle dynamics model is then established to generate high-fidelity physics-based datasets, and a residual neural network is adopted to avoid performance degradation in deep networks. The model is validated and optimized via real-vehicle sandy terrain tests, with its performance compared against other network structures. The proposed model achieves high prediction accuracy, with engineering-acceptable errors, and outperforms conventional neural networks. The dimensionless framework improves generality, overcoming the weaknesses of traditional and purely data-driven models. This work provides theoretical and statistical support for EV traction control design and tire structure optimization, promoting driving stability and terrain passability in unstructured sandy environments. Full article

Externally excited synchronous machines (EESMs) are a rare-earth-free solution for traction applications. However, variable field excitation and magnetic coupling increase control complexity. Efficient validation of the resulting control functionalities can be carried out using hardware-in-the-loop (HIL) testing, which requires high-fidelity real-time simulation models. This paper presents a semi-analytical, discrete-time EESM model tailored for HIL applications. Nonlinear magnetic saturation and magnetic coupling are captured using an inverted flux–current characteristic combined with a rotating coordinate transformation, which improves resource utilization. Spatial harmonics are included through a Fourier decomposition of the angle-dependent inverse characteristics. Additionally, different loss mechanisms are considered to accurately represent the physical behavior of the machine. The model is parameterized using finite element analysis (FEA) results from a 100$\mathrm{k}$$\mathrm{W}$ salient-pole EESM. It is implemented on a field-programmable gate array to achieve real-time capability at a simulation frequency of $2.5$$\mathrm{M}$$\mathrm{Hz}$. Validation results for the typical operating range show deviations below $0.1$% compared to detailed FEA results, demonstrating accurate real-time simulation of the electromagnetic behavior. Full article

This paper presents a computational study of wave–bathymetry and wave–structure interaction problems using advanced numerical techniques based on high-fidelity, two-phase Navier–Stokes (TpNS) flow and reduced-order, fully nonlinear potential flow models. For high-fidelity simulations, the TpNS equations are discretized using the finite-element method, with free-surface evolution captured through a hybrid level-set (LS) and volume-of-fluid (VOF) formulation. A monolithic, phase-conservative LS equation is introduced to mitigate mass loss and interface smearing, combined with a semi-implicit projection scheme. Hydrodynamic forces are resolved using a high-order, phase-resolving cut finite-element method (CutFEM), which enables the representation of complex solid geometries within a fixed background mesh. An equivalent polynomial of Heaviside and Dirac distributions ensures accurate evaluation of surface and volume integrals. Hence, no explicit generation of cut cell meshes, adaptive quadrature, or local refinement is required. For reduced-order modeling, a fast regularized boundary integral method (RBIM) is employed to solve the fully nonlinear potential flow. Singular and near-singular integrals are treated using a subtract-and-addition technique based on auxiliary functions derived from Stokes’ theorem, allowing direct application of high-order quadrature without conventional boundary element discretization. An arbitrary Lagrangian–Eulerian (ALE) formulation is adopted to enforce free-surface boundary conditions while avoiding excessive mesh distortion. The proposed approaches are applied to investigate highly nonlinear wave transformation over complex bathymetry and wave-induced dynamics of floating structures, including eddy-making damping effects. Numerical results are validated against experimental measurements. These two modeling approaches represent complementary levels of physical fidelity and computational efficiency, and their systematic comparison clarifies the trade-offs between computational accuracy, efficiency, and cost for practical marine problems. Full article

Unlike traditional marine floating platforms, floating offshore wind turbines (FOWTs) are subjected to larger overturning moments. This study presents a novel floating offshore wind turbine concept—termed the Multi-Body Anti-Pitching Floating Wind Turbine (MAFWT)—designed to mitigate excessive pitching motion of semi-submersible FOWTs. The MAFWT integrates three Wave-star-like appendages arranged in the UMaine VolturnUS-S platform. A fully coupled dynamic model is developed within the FAST-to-AQWA (F2A) simulation framework. Parametric time- and frequency-domain analyses are subsequently conducted under both regular wave/steady wind and irregular wave/turbulent wind conditions to investigate the influence of stiffness parameter K and damping parameter B on system dynamics. Results demonstrate that increasing stiffness enhances the restoring moment, thereby reducing the static pitching offset and overall dynamic response (with the maximum and average values decreasing by 27.6% and 31.9%, respectively). However, it may amplify low-frequency slow-drift motions (with the maximum and average values of surge increasing by 9.4% and 9.5%, respectively). In contrast, damping primarily dissipates kinetic energy, yielding up to a 25.5% reduction in pitch angular velocity and significantly mitigating power output fluctuations (the standard deviation decreased by 16.4%). Furthermore, increases in the stiffness coefficient and damping coefficient result in respective slight increments of 0.12% and 0.18% in the average power output. This work elucidates the distinct physical mechanisms through which stiffness and damping govern pitch suppression. Full article

Heavy-load unmanned aerial vehicles (UAVs) are increasingly being applied in logistics, infrastructure installation, and emergency response missions, where complex payload dynamics and unstructured environments pose significant challenges to safe and efficient operation. Conventional manual teleoperation interfaces, such as dual-joystick control, impose a high cognitive workload and provide limited support for expressing high-level operator intent, while fully autonomous solutions remain difficult to deploy reliably under real-world uncertainty. To address these limitations, this paper proposes the Multimodal Fusion Cooperation Network (MFCN), an end-to-end shared autonomy framework that integrates speech commands, visual gestures, and haptic cues through cross-modal feature fusion to infer operator intent in real time. The fused intent representation is translated into dynamically feasible control commands by a cooperative control policy with embedded physics-aware constraints to suppress payload oscillations and ensure flight stability. Extensive semi-physical simulations and real-world experiments demonstrate that the MFCN significantly improves the task success rate, positioning accuracy, and payload stability while reducing the task completion time and operator cognitive workload compared with manual, unimodal, and heuristic multimodal baselines. Full article

PET scans have long been used in oncology imaging to provide molecular and metabolic information about diseases. The use of artificial intelligence (AI) in PET scans in oncology theranostics has the potential to optimise PET modality and overcome the constraints that PET scans have, such as semi-quantitative metrics, reader subjectivity, and variability across scanners/institutions. Advances in AI and radiomics are overcoming those limitations by deep learning lesion detection, enhancing image reconstruction, and improving noise resolution, which allows ultra-low dose acquisitions, while physics-informed models integrate with PET systems to strengthen interpretability and quantitative accuracy. There are also predictive AI frameworks that link PET imaging biomarkers to therapy response and outcomes, create individualised care and are even able to simulate treatment response and help with treatment planning. However, challenges do exist. Most AI PET studies are retrospective, single-centre, and underpowered (small sample), with limited external validation and inconsistent standardisation (in acquisition, segmentation, and extraction), leading to poor reproducibility and higher performance estimates. Furthermore, ethical considerations, including data protection and transparency, need to be considered before implementation. Federated learning, physics-informed frameworks, and adherence to standardised protocols offer steps towards regulated AI systems. In summary, PET is evolving from an imaging modality to a platform with the integration of deep learning, radiomics and reconstruction capable of predicting treatment response and guiding treatment. With rigorous prospective validation, cross-institutional collaboration, and regulatory standardisation, AI in PET would create an advancement in nuclear medicine imaging in oncology. Full article

Background and Objectives: Stroke is a leading cause of long-term disability in older adults, who often face persistent motor, cognitive, and functional challenges. Conventional stroke rehabilitation programs often involve highly repetitive motor tasks, which may reduce patient motivation and contribute to suboptimal adherence over time. Virtual reality (VR) offers an engaging alternative; however, much of the existing research has focused on specialized rehabilitation-oriented VR systems rather than off-the-shelf commercial platforms. This study evaluated older stroke survivors’ acceptance, tolerability, and lived experiences of a short VR-based rehabilitation session using a commercial game on a commercial wearable VR system. Methods: A single-session convergent mixed-methods design was employed. Thirteen community-dwelling older stroke survivors (mean age 79.2 ± 7.1 years; 9 males, 4 female) completed a 15 min VR session using a commercial wearable VR system. The Technology Acceptance Model (TAM) questionnaire and Simulator Sickness Questionnaire (SSQ) assessed acceptance and tolerability, while semi-structured interviews explored lived experiences. Qualitative data were thematically analyzed. Results: Participants reported high acceptance across all TAM domains (overall M = 4.35 ± 0.79, scale 1–5). Enjoyment/intention to use was rated highest (M = 4.77 ± 0.42), while perceived usefulness was lowest (M = 4.15 ± 0.77). VR was well tolerated: the SSQ total score was 17.38 ± 1.73, with most symptoms rated at the mild level only. Exploratory Spearman correlations revealed a significant positive association between age and SSQ total score (r_h = +0.568, p = 0.043). Thematic analysis identified five themes: (1) usability and accessibility; (2) therapeutic value; (3) engagement and motivation; (4) social and clinical support; and (5) physical and cognitive demands. Conclusions: A commercial wearable VR system was found to be acceptable, safe, and engaging for older stroke survivors. With supervision and therapeutic framing, it may serve as a motivating adjunct to conventional rehabilitation. Full article

Rapid and accurate recognition of radar active jamming is a prerequisite for cognitive electronic countermeasures. However, under complex electromagnetic environments with scarce training samples, existing deep learning models are prone to capturing spurious correlations induced by environmental confounders, resulting in notable performance degradation. To address this causal confounding issue, we propose a physics-guided variational causal intervention network (PG-VCIN). First, we reconstruct a structured causal model of jamming signal generation, decoupling observations into robust physical statistical features and sensitive time–frequency image representations. Physical priors are then leveraged to perform dynamic precision-weighted modulation of visual feature extraction, enforcing physical consistency at the representation learning stage. Second, we formulate deconfounding within an active inference framework and introduce a variational information bottleneck to optimize mutual information, thereby filtering out high-complexity redundant information attributable to confounders while preserving the essential causal semantics. Finally, we numerically approximate the causal effect by imposing dual intervention constraints in the latent space, including intra-class invariance and confounder invariance. Experiments on a semi-physical simulation dataset demonstrate that the proposed method achieves substantially higher recognition accuracy than several representative few-shot baselines in extremely low-sample regimes, validating the effectiveness of integrating physical mechanisms with causal inference. Full article

Physical separation between family members arises not only from life choices such as education and employment, but also from health-related constraints that limit physical co-presence. This paper presents StepsConnect, a real-time step-sensing-based ambient display system that transforms personal walking data into dynamic digital art, providing low-effort and non-intrusive presence cues for family members living apart. The system continuously captures step data via smartphones and renders them as spatial and embodied visual cues embedded in everyday environments. We conducted a 90 min laboratory study with 15 young adult–parent dyads, in which young adults engaged in a simulated work session while viewing real-time visualizations of their parents’ step activity. Young adults’ perceived connectedness was measured using the Inclusion of Other in the Self (IOS) scale and complemented with semi-structured interviews, while parents’ walking data were logged to provide an objective behavioral reference. Quantitative results indicated modest and heterogeneous changes in IOS scores at the group level, with individual variability across participants. Qualitative findings suggested that step-based visualizations primarily functioned as ambient reminders and cues of presence, supporting momentary relational awareness while remaining calm and non-intrusive within the workspace context. Walking data exhibited large variation across dyads, providing objective context for participants’ subjective experience of presence, although connectedness was not simply proportional to activity magnitude. The findings suggest that aesthetic step-based ambient visualization primarily supports momentary relational awareness rather than immediate shifts in stable closeness. By clarifying this distinction, the study advances understanding of how sensing-based digital art may function as a complementary presence layer in intergenerational contexts. Full article

Response latency and sensor noise are universal challenges in closed-loop control systems. In the context of semi-active suspensions, these issues also exist and manifest as critical bottlenecks. Due to the highly transient nature of road shocks, the inherent physical actuation delays of the hardware, combined with the phase lag introduced by traditional signal filtering, often cause the control response to significantly lag behind the physical excitation. To address this issue from a predictive perspective, this study proposes a Physics-Informed Gated Convolutional Neural Network (PI-GCNN) designed to predict future multi-modal energy evolution, thereby enabling feedforward control. Unlike traditional feedback mechanisms, the proposed framework employs the Continuous Wavelet Transform (CWT) to convert short-horizon inertial data into time–frequency scalograms, effectively isolating transient shock features from background vibrations. A novel physics-guided gating mechanism is embedded within the network architecture to regulate feature activation. This mechanism is trained using an asymmetric sparse physics loss, which combines L1 regularization with adaptive spectral consistency constraints to enforce noise suppression on flat roads while ensuring sensitivity to impacts. Extensive validation was conducted using high-fidelity heavy truck simulations and the public PVS 9 real-world dataset. The results confirm that the PI-GCNN achieves a predictive phase lead of approximately 100–200 ms over real-time baselines, creating a valuable actuation window for suspension dampers. Furthermore, the model demonstrates exceptional computational efficiency, with a parameter count of 0.10 M and a single-frame inference latency of 0.25 ms, making it highly suitable for deployment on resource-constrained automotive edge computing platforms. Full article

While anisotropic extrudate swell in polymer processing is fundamentally driven by physical viscoelastic recovery, this paper proposes a theoretical framework to explicitly isolate and map the purely geometric and kinematic components of this phenomenon. Serving as a mathematical proof-of-concept, a multi-velocity-centre hypothesis is proposed. By introducing a semi-empirical, lumped material-flow calibration parameter, the macroscopic diameter swell ratio is mathematically extended to the discrete local flow field of a rectangular slit die. To evaluate its validity, the analytical framework is subjected to a numerical test for kinematic consistency utilizing isothermal, inelastic power-law fluid CFD simulations, thereby separating geometric mapping from complex viscoelastic stress relaxation. Results indicate that analytical predictions show good agreement with CFD data (error < 5%) strictly within the core zone of high-aspect-ratio dies. However, due to the infinite-slit assumption, 3D flow kinematics near die edges induce velocity decay, leading to local deviations that require future empirical corrections. Although comprehensive physical extrusion experiments and non-isothermal viscoelastic coupling are required for industrial deployment, this semi-empirical kinematic mapping provides a foundational mathematical basis that could potentially inform future inverse die-profile design and shape distortion compensation. Full article